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  • University of Science - VNUHCM
  • Ho Chi Minh City
  • 20:20 (UTC +07:00)
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Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: Pull Shark

Achievements

Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: Pull Shark

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lqb464/README.md

🧭 At a Glance

↳ auto-updates from public repos only. Cards not loading? See note below.


I build reproducible ML pipelines — from applied AI products to production-ready model serving. Current focus: shipping AI in apps (FastAPI, Docker, full-stack) and classical ML foundations.


🧭 Career tracks

Track What I do Projects Status
📊 Data Scientist Tabular ML · clustering · imbalanced · notebooks 5 🔵 Growing
🤖 AI Engineer Agents · RAG · generative · virtual try-on · full-stack 4 🔵 Growing

9 projects across 2 tracks · full index →


🛠️ Stack

🤖 AI Engineer

PyTorch FastAPI Docker PostgreSQL

📊 Data Scientist

scikit-learn Pandas Jupyter XGBoost

↳ Full skills breakdown — honest levels, mapped to projects


⭐ Highlights

📊 Data Scientist

Titanic House Prices Iris MNIST Credit Card Fraud

5 notebook-driven ML foundations — classification, regression, clustering, imbalanced data.

🤖 AI Engineer

AgenThink RAnythinG SketClothes VirtuaLook

4 shipped apps — agents · local RAG · generative · virtual try-on.

💻 Most Used Languages

↳ public repos only


🗺️ On the roadmap

Not claiming as production skills yet:

  • CUDA · TensorRT · GPU inference optimization
  • Kubernetes · production-grade MLOps · CI/CD at scale
  • Apache Spark · Airflow · cloud data engineering
  • Cloud certifications (AWS / GCP)

📫 Connect  GitHub

© 2026 Luong Quoc Binh · Ho Chi Minh City, Vietnam


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Popular repositories Loading

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  2. mnist-digit-recognizer mnist-digit-recognizer Public

    End-to-end MNIST digit classification: EDA, model selection (baselines + MLP), full retrain, model persistence, CLI inference, and Kaggle submission. Two-phase pipeline with documented metrics and …

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  3. house-prices-regression house-prices-regression Public

    Full regression lifecycle on Ames Housing: sklearn preprocessing, baseline models, Optuna tuning, stacking ensemble, and Kaggle submission. Modular Python pipeline with holdout evaluation and expor…

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    Binary survival prediction from exploration to deployment: EDA and feature engineering in notebooks, reproducible training via CLI, saved model, and Kaggle submission. Holdout accuracy ~85% with Lo…

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  5. iris-kmeans-clustering iris-kmeans-clustering Public

    Unsupervised learning lifecycle on Iris: scaling, elbow and silhouette analysis, K-Means training, cluster evaluation, model persistence, and CLI inference on new samples. Notebook exploration plus…

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  6. RNN-With-Attention-For-Machine-Translation-From-Scratch RNN-With-Attention-For-Machine-Translation-From-Scratch Public

    Forked from HoangVo-Prog/machine-translation-from-scratch

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